http://dx.doi.org/10.1504/IJDMB.2009.026700">
 

Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

A semi-supervised approach to projected clustering with applications to microarray data

Language

English

Abstract

Recent studies have suggested that extremely low dimensional projected clusters exist in real datasets. Here, we propose a new algorithm for identifying them. It combines object clustering and dimension selection, and allows the input of domain knowledge in guiding the clustering process. Theoretical and experimental results show that even a small amount of input knowledge could already help detect clusters with only 1% of the relevant dimensions. We also show that this semi-supervised algorithm can perform knowledge-guided selective clustering when there are multiple meaningful object groupings. The algorithm is also shown effective in analysing a microarray dataset.

Publication Date

2009

Source Publication Title

International Journal of Data Mining and Bioinformatics

Volume

3

Issue

3

Start Page

229

End Page

259

Publisher

Inderscience

ISSN (print)

17485673

ISSN (electronic)

17485681

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